In the semiconductor industry, there is a vast amount of sensor data for the various tools running recipes. Typically, the sensor information is raw data, which generally may not be helpful to some users, such as process engineers, etc. The large amount of data can often times be difficult to manage. Some solutions use a statistical approach, such as PCA (principal component analysis), to attempt to transform the raw data into meaningful data for users. However, the unique characteristics of the semiconductor processes, such as non-linearity in batch processes, process steps with variable duration, etc., have posed some difficulties in the PCA-based solutions.